Head, Data & Intelligence Engineering
Summary
Lead data platforms, analytics, and AI capabilities at a bank, directing engineering, BI, and governance teams. Oversee scalable pipelines, ML models, and regulatory compliance.
About the
Role
The Head, Data &
Intelligence Engineering owns the data platforms, analytics, and AI
capabilities that power decision-making and personalized customer experiences
at Polaris Bank. This is a leadership role directing four specialist
disciplines — Data Engineering, Analytics & BI, Data Science & AI, and
Data Governance — through dedicated leads and engineers, not as a hands-on
individual contributor across all four.
Key
Responsibilities
Data
Engineering Oversight
- Direct the design of scalable,
reliable data pipelines, warehouses, and ETL infrastructure
- Set standards for data modeling and
infrastructure architecture used across the bank's data platforms
Analytics
& BI Oversight
- Ensure the analytics function delivers
dashboards, reports, and self-service tools that drive data-driven
decisions across the bank
- Set standards for data visualization
and reporting consistency
Data
Science & AI Oversight
- Direct the development of AI/ML models
supporting personalization, fraud detection, credit scoring, and operational
optimization
- Ensure model performance, fairness,
and reliability are validated before production deployment
Data
Governance Oversight
- Ensure data quality, privacy, and
metadata management practices are enforced across all data assets
- Own the bank's data governance
framework and ensure regulatory compliance in data handling
Core
Competencies
- Data platform strategy and technical
leadership across engineering, analytics, and data science
- Working fluency in ML/AI model
lifecycle management and MLOps practices
- Strong grounding in data privacy and
regulatory compliance (NDPR and applicable banking data regulations)
- Stakeholder management across
technology, risk, and business functions
Familiarity
With Tools (used by the function's teams)
Python, SQL, Apache
Spark, Airflow, Kafka, Databricks, Snowflake, AWS Glue, dbt; Power BI, Tableau,
Looker, Metabase; TensorFlow, PyTorch, Scikit-learn, MLflow, SageMaker,
Kubeflow; Collibra, Informatica
Requirements
Qualifications
- Bachelor's degree in Computer Science,
Data Science, Statistics, or related field; advanced degree an advantage
- Demonstrated track record leading data
engineering, analytics, or data science functions, ideally in banking or
financial services